6 citations · 6 across the 2 of their papers we have counts for
4 papers
Atomistic graph networks for experimental materials property prediction
Tian Xie, Victor Bapst, Alexander L. Gaunt +5
Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Mat…
GraphHop: An Enhanced Label Propagation Method for Node Classification
Tian Xie, Bin Wang, C. -C. Jay Kuo
A scalable semi-supervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains attributes of all nodes but labels of…
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Chaoyang He, Tian Xie, Yu Rong +4
Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more…
Domain Representation for Knowledge Graph Embedding
Cunxiang Wang, Feiliang Ren, Zhichao Lin +3
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However,…